This study, conducted by Zeng et al., focuses on how artificial intelligence methods could aid in overcoming persistent challenges in Chinese epidemiological studies. Rather than collecting new data, the authors conducted an extensive review of existing literature to evaluate the current landscape of large-scale cohort studies in China. Through this, they found multiple hurdles for Chinese cohort studies. They found multiple obstacles, including limited data standardization, uneven infrastructure, and sustainability issues. They proposed multiple strategic priorities to overcome these challenges. They proposed to prioritize AI infrastructure development in rural regions, design interdisciplinary “AI for the healthcare” curricula, establish national data standardization frameworks and interoperability standards, and establish an AI validation framework for longitudinal research. They found that integration of AI into cohort studies demands a robust framework that upholds fairness and a commitment to transparency. Overall, their goal is to evolve China’s national strategic framework through advances in medical AI and digital health and make it more accessible to the larger population. They want to spread their studies to inform researchers, healthcare providers, and policy makers seeking to advance the scientific rigor, equity, and translational impact of cohort studies in China.